• DocumentCode
    947088
  • Title

    The Meaning and Use of the Volume Under a Three-Class ROC Surface (VUS)

  • Author

    He, Xin ; Frey, Eric C.

  • Author_Institution
    Johns Hopkins Univ., Baltimore
  • Volume
    27
  • Issue
    5
  • fYear
    2008
  • fDate
    5/1/2008 12:00:00 AM
  • Firstpage
    577
  • Lastpage
    588
  • Abstract
    Previously, we have proposed a method for three-class receiver operating characteristic (ROC) analysis based on decision theory. In this method, the volume under a three-class ROC surface (VUS) serves as a figure-of-merit (FOM) and measures three-class task performance. The proposed three-class ROC analysis method was demonstrated to be optimal under decision theory according to several decision criteria. Further, an optimal three-class linear observer was proposed to simultaneously maximize the signal-to-noise ratio (SNR) between the test statistics of each pair of the classes provided certain data linearity condition. Applicability of this three-class ROC analysis method would be further enhanced by the development of an intuitive meaning of the VUS and a more general method to calculate the VUS that provides an estimate of its standard error. In this paper, we investigated the general meaning and usage of VUS as a FOM for three-class classification task performance. We showed that the VUS value, which is obtained from a rating procedure, equals the percent correct in a corresponding categorization procedure for continuous rating data. The significance of this relationship goes beyond providing another theoretical basis for three-class ROC analysis - it enables statistical analysis of the VUS value. Based on this relationship, we developed and tested algorithms for calculating the VUS and its variance. Finally, we reviewed the current status of the proposed three-class ROC analysis methodology, and concluded that it extends and unifies decision theoretic, linear discriminant analysis, and psychophysical foundations of binary ROC analysis in a three-class paradigm.
  • Keywords
    biomedical imaging; decision theory; image classification; medical image processing; sensitivity analysis; statistical analysis; data linearity condition; decision theory; linear discriminant analysis; medical diagnostic task; optimal three-class linear observer; psychophysical foundations; signal-to-noise ratio; statistical analysis; three-class ROC surface volume; three-class classification task; three-class receiver operating characteristic analysis; Ideal observer; ROC analysis; receiver operating characteristic (ROC) analysis; three-class classification; Algorithms; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; ROC Curve; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/TMI.2007.908687
  • Filename
    4359076